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!12743 Add ERROR and WARNING information for execute class

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From: @lizhenglong1992
Reviewed-by: @heleiwang,@pandoublefeng
Signed-off-by: @pandoublefeng
pull/12743/MERGE
mindspore-ci-bot 4 years ago committed by Gitee
parent 130f4c2810 5cf805d46a
commit d61f646431
5 changed files with 86 additions and 27 deletions
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60
mindspore/ccsrc/minddata/dataset/api/execute.cc
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@ -237,11 +237,20 @@ Status Execute::operator()(const mindspore::MSTensor &input, mindspore::MSTensor
CHECK_FAIL_RETURN_UNEXPECTED(validate_device_(), "Device Type should be 'Ascend310' or 'CPU'");
// Validate and build runtime ops
std::vector<std::shared_ptr<TensorOp>> transforms; // record the transformations
std::map<MapTargetDevice, std::string> env_list = {
{MapTargetDevice::kCpu, "kCpu"}, {MapTargetDevice::kGpu, "kGpu"}, {MapTargetDevice::kAscend310, "kAscend310"}};
for (int32_t i = 0; i < ops_.size(); i++) {
if (ops_[i] == nullptr) {
MS_LOG(ERROR) << "Input TensorOperation["
<< std::to_string(i) + "] is unsupported on your input device:" << env_list.at(device_type_);
}
CHECK_FAIL_RETURN_UNEXPECTED(ops_[i] != nullptr, "Input TensorOperation[" + std::to_string(i) + "] is null");
RETURN_IF_NOT_OK(ops_[i]->ValidateParams());
transforms.emplace_back(ops_[i]->Build());
}
if (device_type_ == MapTargetDevice::kCpu) {
// Convert mindspore::Tensor to dataset::Tensor
std::shared_ptr<dataset::Tensor> de_tensor;
@ -272,11 +281,15 @@ Status Execute::operator()(const mindspore::MSTensor &input, mindspore::MSTensor
} else { // Ascend310 case, where we must set Ascend resource on each operators
#ifdef ENABLE_ACL
CHECK_FAIL_RETURN_UNEXPECTED(device_resource_, "Device resource is nullptr which is illegal under case Ascend310");
// Sink data from host into device
std::shared_ptr<mindspore::dataset::DeviceTensor> device_input;
RETURN_IF_NOT_OK(device_resource_->Sink(input, &device_input));
for (auto &t : transforms) {
// Initialize AscendResource for each operators
std::shared_ptr<DeviceTensor> device_output;
RETURN_IF_NOT_OK(t->SetAscendResource(device_resource_));
RETURN_IF_NOT_OK(t->Compute(device_input, &device_output));
// For next transform
@ -284,7 +297,7 @@ Status Execute::operator()(const mindspore::MSTensor &input, mindspore::MSTensor
}
CHECK_FAIL_RETURN_UNEXPECTED(device_input->HasDeviceData(), "Apply transform failed, output tensor has no data");
std::shared_ptr<mindspore::dataset::Tensor> host_output;
// Need to optimize later, waiting for computing department development, hence we pop data temporarily.
// TODO(lizhenglong) waiting for computing department development, hence we pop data onto host temporarily.
RETURN_IF_NOT_OK(device_resource_->Pop(device_input, &host_output));
*output = mindspore::MSTensor(std::make_shared<DETensor>(host_output));
// *output = mindspore::MSTensor(std::make_shared<DETensor>(device_input, true)); Use in the future
@ -302,9 +315,16 @@ Status Execute::operator()(const std::vector<MSTensor> &input_tensor_list, std::
CHECK_FAIL_RETURN_UNEXPECTED(!ops_.empty(), "Input TensorOperation should be provided");
CHECK_FAIL_RETURN_UNEXPECTED(validate_device_(), "Device Type should be 'Ascend310' or 'CPU'");
std::map<MapTargetDevice, std::string> env_list = {
{MapTargetDevice::kCpu, "kCpu"}, {MapTargetDevice::kGpu, "kGpu"}, {MapTargetDevice::kAscend310, "kAscend310"}};
// Validate and build runtime ops
std::vector<std::shared_ptr<TensorOp>> transforms;
for (int32_t i = 0; i < ops_.size(); i++) {
if (ops_[i] == nullptr) {
MS_LOG(ERROR) << "Input TensorOperation["
<< std::to_string(i) + "] is unsupported on your input device:" << env_list.at(device_type_);
}
CHECK_FAIL_RETURN_UNEXPECTED(ops_[i] != nullptr, "Input TensorOperation[" + std::to_string(i) + "] is null");
RETURN_IF_NOT_OK(ops_[i]->ValidateParams());
transforms.emplace_back(ops_[i]->Build());
@ -340,11 +360,14 @@ Status Execute::operator()(const std::vector<MSTensor> &input_tensor_list, std::
#ifdef ENABLE_ACL
CHECK_FAIL_RETURN_UNEXPECTED(device_resource_, "Device resource is nullptr which is illegal under case Ascend310");
for (auto &input_tensor : input_tensor_list) {
// Sink each data from host into device
std::shared_ptr<dataset::DeviceTensor> device_input;
RETURN_IF_NOT_OK(device_resource_->Sink(input_tensor, &device_input));
for (auto &t : transforms) {
std::shared_ptr<DeviceTensor> device_output;
RETURN_IF_NOT_OK(t->SetAscendResource(device_resource_));
RETURN_IF_NOT_OK(t->Compute(device_input, &device_output));
// For next transform
@ -355,8 +378,10 @@ Status Execute::operator()(const std::vector<MSTensor> &input_tensor_list, std::
// So the speed of this batch method is slower than solo mode
std::shared_ptr<mindspore::dataset::Tensor> host_output;
RETURN_IF_NOT_OK(device_resource_->Pop(device_input, &host_output));
auto ms_tensor = mindspore::MSTensor(std::make_shared<DETensor>(host_output));
output_tensor_list->emplace_back(ms_tensor);
// Release the data on the device because we have copied one piece onto host
RETURN_IF_NOT_OK(device_resource_->DeviceDataRelease());
}
CHECK_FAIL_RETURN_UNEXPECTED(!output_tensor_list->empty(), "Output Tensor vector is empty");
@ -367,11 +392,11 @@ Status Execute::operator()(const std::vector<MSTensor> &input_tensor_list, std::
std::vector<uint32_t> AippSizeFilter(const std::vector<uint32_t> &resize_para, const std::vector<uint32_t> &crop_para) {
std::vector<uint32_t> aipp_size;
if (resize_para.size() == 0) {
if (resize_para.size() == 0) { // If only Crop operator exists
aipp_size = crop_para;
} else if (crop_para.size() == 0) {
} else if (crop_para.size() == 0) { // If only Resize operator exists
aipp_size = resize_para;
} else {
} else { // If both of them exist
if (resize_para.size() == 1) {
aipp_size = *min_element(crop_para.begin(), crop_para.end()) < *resize_para.begin() ? crop_para : resize_para;
} else {
@ -386,7 +411,7 @@ std::vector<uint32_t> AippSizeFilter(const std::vector<uint32_t> &resize_para, c
std::vector<uint32_t> AippMeanFilter(const std::vector<uint32_t> &normalize_para) {
std::vector<uint32_t> aipp_mean;
if (normalize_para.size() == 6) {
if (normalize_para.size() == 6) { // If Normalize operator exist
std::transform(normalize_para.begin(), normalize_para.begin() + 3, std::back_inserter(aipp_mean),
[](uint32_t i) { return static_cast<uint32_t>(i / 10000); });
} else {
@ -397,12 +422,12 @@ std::vector<uint32_t> AippMeanFilter(const std::vector<uint32_t> &normalize_para
std::vector<float> AippStdFilter(const std::vector<uint32_t> &normalize_para) {
std::vector<float> aipp_std;
if (normalize_para.size() == 6) {
if (normalize_para.size() == 6) { // If Normalize operator exist
auto zeros = std::find(std::begin(normalize_para), std::end(normalize_para), 0);
if (zeros == std::end(normalize_para)) {
std::transform(normalize_para.begin() + 3, normalize_para.end(), std::back_inserter(aipp_std),
[](uint32_t i) { return static_cast<float>(10000 / i); });
} else {
[](uint32_t i) { return 10000 / static_cast<float>(i); });
} else { // If 0 occurs in std vector
MS_LOG(WARNING) << "Detect 0 in std vector, please verify your input";
aipp_std = {1.0, 1.0, 1.0};
}
@ -414,6 +439,7 @@ std::vector<float> AippStdFilter(const std::vector<uint32_t> &normalize_para) {
Status AippInfoCollection(std::map<std::string, std::string> *aipp_options, const std::vector<uint32_t> &aipp_size,
const std::vector<uint32_t> &aipp_mean, const std::vector<float> &aipp_std) {
// Several aipp config parameters
aipp_options->insert(std::make_pair("related_input_rank", "0"));
aipp_options->insert(std::make_pair("src_image_size_w", std::to_string(aipp_size[1])));
aipp_options->insert(std::make_pair("src_image_size_h", std::to_string(aipp_size[1])));
@ -422,6 +448,7 @@ Status AippInfoCollection(std::map<std::string, std::string> *aipp_options, cons
aipp_options->insert(std::make_pair("aipp_mode", "static"));
aipp_options->insert(std::make_pair("csc_switch", "true"));
aipp_options->insert(std::make_pair("rbuv_swap_switch", "false"));
// Y = AX + b, this part is A
std::vector<int32_t> color_space_matrix = {256, 0, 359, 256, -88, -183, 256, 454, 0};
int count = 0;
for (int i = 0; i < 3; i++) {
@ -431,19 +458,23 @@ Status AippInfoCollection(std::map<std::string, std::string> *aipp_options, cons
++count;
}
}
// This part is b
std::vector<uint32_t> color_space_bias = {0, 128, 128};
for (int i = 0; i < 3; i++) {
std::string key_word = "input_bias_" + std::to_string(i);
aipp_options->insert(std::make_pair(key_word, std::to_string(color_space_bias[i])));
}
// Y = (X - mean - min) * [std^(-1)], this part is mean
for (int i = 0; i < aipp_mean.size(); i++) {
std::string key_word = "mean_chn_" + std::to_string(i);
aipp_options->insert(std::make_pair(key_word, std::to_string(aipp_mean[i])));
}
// This part is min
for (int i = 0; i < aipp_mean.size(); i++) {
std::string key_word = "min_chn_" + std::to_string(i);
aipp_options->insert(std::make_pair(key_word, "0.0"));
}
// This part is std^(-1)
for (int i = 0; i < aipp_std.size(); i++) {
std::string key_word = "var_reci_chn_" + std::to_string(i);
aipp_options->insert(std::make_pair(key_word, std::to_string(aipp_std[i])));
@ -456,6 +487,7 @@ std::string Execute::AippCfgGenerator() {
#ifdef ENABLE_ACL
std::vector<uint32_t> paras; // Record the parameters value of each Ascend operators
for (int32_t i = 0; i < ops_.size(); i++) {
// Validate operator ir
json ir_info;
if (ops_[i] == nullptr) {
MS_LOG(ERROR) << "Input TensorOperation[" + std::to_string(i) + "] is null";
@ -465,6 +497,7 @@ std::string Execute::AippCfgGenerator() {
MS_LOG(ERROR) << "Input TensorOperation[" + std::to_string(i) + "] has wrong parameters";
return "";
}
// Define map between operator name and parameter name
ops_[i]->to_json(&ir_info);
std::multimap<std::string, std::string> op_list = {{vision::kDvppCropJpegOperation, "size"},
{vision::kDvppDecodeResizeOperation, "size"},
@ -473,28 +506,34 @@ std::string Execute::AippCfgGenerator() {
{vision::kDvppNormalizeOperation, "mean"},
{vision::kDvppNormalizeOperation, "std"},
{vision::kDvppResizeJpegOperation, "size"}};
// Collect the information of operators
for (auto pos = op_list.equal_range(ops_[i]->Name()); pos.first != pos.second; ++pos.first) {
auto paras_key_word = pos.first->second;
paras = ir_info[paras_key_word].get<std::vector<uint32_t>>();
info_->aipp_cfg_.insert(std::make_pair(ops_[i]->Name(), paras));
}
}
std::ofstream outfile;
outfile.open(config_location, std::ofstream::out);
if (!outfile.is_open()) {
MS_LOG(ERROR) << "Fail to open Aipp config file, please verify your system config(including authority)"
<< "We will return empty string which represent the location of Aipp config file in this case";
std::string except = "";
return except;
}
if (device_type_ == MapTargetDevice::kAscend310) {
// Process resize parameters and crop parameters to find out the final size of input data
std::vector<uint32_t> resize_paras;
std::vector<uint32_t> crop_paras;
// Find resize parameters
auto iter = info_->aipp_cfg_.find(vision::kDvppResizeJpegOperation);
if (iter != info_->aipp_cfg_.end()) {
resize_paras = iter->second;
}
// Find crop parameters
iter = info_->aipp_cfg_.find(vision::kDvppCropJpegOperation);
if (iter != info_->aipp_cfg_.end()) {
crop_paras = iter->second;
@ -502,7 +541,9 @@ std::string Execute::AippCfgGenerator() {
crop_paras.emplace_back(crop_paras[0]);
}
}
std::vector<uint32_t> aipp_size = AippSizeFilter(resize_paras, crop_paras);
// Process normalization parameters to find out the final normalization parameters for Aipp module
std::vector<uint32_t> normalize_paras;
if (info_->aipp_cfg_.find(vision::kDvppNormalizeOperation) != info_->aipp_cfg_.end()) {
@ -512,10 +553,13 @@ std::string Execute::AippCfgGenerator() {
normalize_paras.insert(normalize_paras.end(), mean_or_std.begin(), mean_or_std.end());
}
}
std::vector<uint32_t> aipp_mean = AippMeanFilter(normalize_paras);
std::vector<float> aipp_std = AippStdFilter(normalize_paras);
std::map<std::string, std::string> aipp_options;
AippInfoCollection(&aipp_options, aipp_size, aipp_mean, aipp_std);
std::string tab_char(4, ' ');
outfile << "aipp_op {" << std::endl;
for (auto &option : aipp_options) {

1
mindspore/ccsrc/minddata/dataset/include/samplers.h
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@ -25,7 +25,6 @@
namespace mindspore {
namespace dataset {
// Abstract class to represent a sampler in the data pipeline.
/// \class Sampler samplers.h
/// \brief An abstract base class to represent a sampler in the data pipeline.

4
mindspore/ccsrc/minddata/dataset/include/vision_ascend.h
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@ -32,10 +32,6 @@ namespace dataset {
// Transform operations for performing computer vision.
namespace vision {
class DvppDecodeResizeOperation;
class DvppDecodeResizeCropOperation;
class DvppDecodePngOperation;
/* ##################################### API class ###########################################*/
class DvppDecodeResizeJpeg : public TensorTransform {

18
mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/utils/MDAclProcess.cc
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@ -222,6 +222,9 @@ APP_ERROR MDAclProcess::D2H_Pop(const std::shared_ptr<mindspore::dataset::Device
}
APP_ERROR MDAclProcess::JPEG_D(const RawData &ImageInfo) {
MS_LOG(WARNING) << "It's deprecated to use kCpu as input device for Dvpp operators to compute, because it's slow and "
"unsafe, we recommend you to set input device as MapTargetDevice::kAscend for Dvpp operators. "
"This API will be removed later";
struct timeval begin = {0};
struct timeval end = {0};
gettimeofday(&begin, nullptr);
@ -307,6 +310,9 @@ APP_ERROR MDAclProcess::JPEG_D_() {
}
APP_ERROR MDAclProcess::JPEG_R(const DvppDataInfo &ImageInfo) {
MS_LOG(WARNING) << "It's deprecated to use kCpu as input device for Dvpp operators to compute, because it's slow and "
"unsafe, we recommend you to set input device as MapTargetDevice::kAscend for Dvpp operators. "
"This API will be removed later";
struct timeval begin = {0};
struct timeval end = {0};
gettimeofday(&begin, nullptr);
@ -414,6 +420,9 @@ APP_ERROR MDAclProcess::JPEG_R_(std::string &last_step) {
}
APP_ERROR MDAclProcess::JPEG_C(const DvppDataInfo &ImageInfo) {
MS_LOG(WARNING) << "It's deprecated to use kCpu as input device for Dvpp operators to compute, because it's slow and "
"unsafe, we recommend you to set input device as MapTargetDevice::kAscend for Dvpp operators. "
"This API will be removed later";
struct timeval begin = {0};
struct timeval end = {0};
gettimeofday(&begin, nullptr);
@ -544,6 +553,9 @@ APP_ERROR MDAclProcess::JPEG_C_(std::string &last_step) {
}
APP_ERROR MDAclProcess::PNG_D(const RawData &ImageInfo) {
MS_LOG(WARNING) << "It's deprecated to use kCpu as input device for Dvpp operators to compute, because it's slow and "
"unsafe, we recommend you to set input device as MapTargetDevice::kAscend for Dvpp operators. "
"This API will be removed later";
struct timeval begin = {0};
struct timeval end = {0};
gettimeofday(&begin, nullptr);
@ -633,6 +645,9 @@ APP_ERROR MDAclProcess::PNG_D_() {
* @return: aclError which is error code of ACL API
*/
APP_ERROR MDAclProcess::JPEG_DRC(const RawData &ImageInfo) {
MS_LOG(WARNING) << "It's deprecated to use kCpu as input device for Dvpp operators to compute, because it's slow and "
"unsafe, we recommend you to set input device as MapTargetDevice::kAscend for Dvpp operators. "
"This API will be removed later";
struct timeval begin = {0};
struct timeval end = {0};
gettimeofday(&begin, nullptr);
@ -766,6 +781,9 @@ APP_ERROR MDAclProcess::JPEG_DRC_(const RawData &ImageInfo) {
}
APP_ERROR MDAclProcess::JPEG_DR(const RawData &ImageInfo) {
MS_LOG(WARNING) << "It's deprecated to use kCpu as input device for Dvpp operators to compute, because it's slow and "
"unsafe, we recommend you to set input device as MapTargetDevice::kAscend for Dvpp operators. "
"This API will be removed later";
struct timeval begin = {0};
struct timeval end = {0};
gettimeofday(&begin, nullptr);

30
tests/st/cpp/dataset/test_de.cc
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@ -44,11 +44,11 @@ TEST_F(TestDE, TestResNetPreprocess) {
auto image = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
// Define transform operations
auto decode(new vision::Decode());
auto resize(new vision::Resize({224, 224}));
auto normalize(
std::shared_ptr<TensorTransform> decode(new vision::Decode());
std::shared_ptr<TensorTransform> resize(new vision::Resize({224, 224}));
std::shared_ptr<TensorTransform> normalize(
new vision::Normalize({0.485 * 255, 0.456 * 255, 0.406 * 255}, {0.229 * 255, 0.224 * 255, 0.225 * 255}));
auto hwc2chw(new vision::HWC2CHW());
std::shared_ptr<TensorTransform> hwc2chw(new vision::HWC2CHW());
mindspore::dataset::Execute Transform({decode, resize, normalize, hwc2chw});
@ -73,7 +73,7 @@ TEST_F(TestDE, TestDvpp) {
// Define dvpp transform
std::vector<uint32_t> crop_paras = {224, 224};
std::vector<uint32_t> resize_paras = {256, 256};
auto decode_resize_crop(new vision::DvppDecodeResizeCropJpeg(crop_paras, resize_paras));
std::shared_ptr<TensorTransform> decode_resize_crop(new vision::DvppDecodeResizeCropJpeg(crop_paras, resize_paras));
mindspore::dataset::Execute Transform(decode_resize_crop, MapTargetDevice::kAscend310);
// Apply transform on images
@ -92,7 +92,7 @@ TEST_F(TestDE, TestDvpp) {
real_h = (crop_paras[0] % 2 == 0) ? crop_paras[0] : crop_paras[0] + 1;
real_w = (remainder == 0) ? crop_paras[1] : crop_paras[1] + 16 - remainder;
}
/* Use in the future
/* TODO Use in the future after compute college finish their job
ASSERT_EQ(image.Shape()[0], real_h); // For image in YUV format, each pixel takes 1.5 byte
ASSERT_EQ(image.Shape()[1], real_w);
ASSERT_EQ(image.DataSize(), real_h * real_w * 1.5);
@ -117,8 +117,8 @@ TEST_F(TestDE, TestDvppSinkMode) {
std::shared_ptr<TensorTransform> decode(new vision::Decode());
std::shared_ptr<TensorTransform> resize(new vision::Resize(resize_paras));
std::shared_ptr<TensorTransform> centercrop(new vision::CenterCrop(crop_paras));
std::vector<std::shared_ptr<TensorTransform>> transforms = {decode, resize, centercrop};
mindspore::dataset::Execute Transform(transforms, MapTargetDevice::kAscend310);
std::vector<std::shared_ptr<TensorTransform>> trans_list = {decode, resize, centercrop};
mindspore::dataset::Execute Transform(trans_list, MapTargetDevice::kAscend310);
// Apply transform on images
Status rc = Transform(image, &image);
@ -155,12 +155,14 @@ TEST_F(TestDE, TestDvppDecodeResizeCropNormalize) {
std::vector<int32_t> resize_paras = {512};
std::vector<float> mean = {0.485 * 255, 0.456 * 255, 0.406 * 255};
std::vector<float> std = {0.229 * 255, 0.224 * 255, 0.225 * 255};
auto decode(new vision::Decode());
auto resize(new vision::Resize(resize_paras));
auto centercrop(new vision::CenterCrop(crop_paras));
auto normalize(new vision::Normalize(mean, std));
std::vector<TensorTransform *> trans_lists = {decode, resize, centercrop, normalize};
mindspore::dataset::Execute Transform(trans_lists, MapTargetDevice::kAscend310);
std::shared_ptr<TensorTransform> decode(new vision::Decode());
std::shared_ptr<TensorTransform> resize(new vision::Resize(resize_paras));
std::shared_ptr<TensorTransform> centercrop(new vision::CenterCrop(crop_paras));
std::shared_ptr<TensorTransform> normalize(new vision::Normalize(mean, std));
std::vector<std::shared_ptr<TensorTransform>> trans_list = {decode, resize, centercrop, normalize};
mindspore::dataset::Execute Transform(trans_list, MapTargetDevice::kAscend310);
std::string aipp_cfg = Transform.AippCfgGenerator();
ASSERT_EQ(aipp_cfg, "./aipp.cfg");

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